Take-home Exercise 3 - MC3

Author

Shaun Tan

Published

June 3, 2023

Modified

June 18, 2023

1. The Task

1.1 Background

The country of Oceanus has sought FishEye International’s help in identifying companies possibly engaged in illegal, unreported, and unregulated (IUU) fishing. As part of the collaboration, FishEye’s analysts received import/export data for Oceanus’ marine and fishing industries. However, Oceanus has informed FishEye that the data is incomplete. To facilitate their analysis, FishEye transformed the trade data into a knowledge graph. Using this knowledge graph, they hope to understand business relationships, including finding links that will help them stop IUU fishing and protect marine species that are affected by it. FishEye analysts found that node-link diagrams gave them a good high-level overview of the knowledge graph. However, they are now looking for visualizations that provide more detail about patterns for entities in the knowledge graph

1.2 The Task in detail

Use visual analytics to identify anomalies in the business groups present in the knowledge graph. Limit your response to 400 words and 5 images.

2. Data Prep

2.1 Loading the requisite R libraries:

Show the code

pacman::p_load(jsonlite, tidygraph, ggraph, visNetwork, gralayouts, ggforce, tidytext, tidyverse, skimr, patchwork, ggdist, ggridges, ggthemes, scales)

2.2 Importing the requisite JSON files

Show the code

mc3_data <-
  jsonlite::fromJSON("data/MC3.json")

2.2 Extracting Edges

Show the code

mc3_edges <-
  as_tibble(mc3_data$links) %>%
    distinct() %>%
  mutate(source = as.character(source),
         target = as.character(target),
         type = as.character(type)) %>%
  group_by(source, target, type) %>%
  summarise(weights = n()) %>%
  filter(source!=target) %>%
  ungroup()

2.3 Extracting Nodes

Show the code

mc3_nodes <- as_tibble(mc3_data$nodes) %>%
# distinct() %>%
  mutate(country = as.character(country),
         id = as.character(id),
         product_services = as.character(product_services),
         revenue_omu = as.numeric(as.character(revenue_omu)),
         type = as.character(type)) %>%
  select(id, country, type, revenue_omu, product_services)

3. Initial Data Exploration

3.1 Exploring Edges

Show the code

skim(mc3_edges)

Data summary
Name	mc3_edges
Number of rows	24036
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
source	1	6	700	12856
target	1	6	28	21265
type	1	16	16	2

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weights	0	1	1	0	1	1	1	1	1	▁▁▇▁▁

Show the code

DT::datatable(mc3_edges)

Show the code

ggplot(data = mc3_edges,
       aes(x = type)) +
  geom_bar()

3.2 Exploring the nodes data frame

Show the code

skim(mc3_nodes)

Data summary
Name	mc3_nodes
Number of rows	27622
Number of columns	5
_______________________
Column type frequency:
character	4
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
id	1	6	64	22929
country	1	2	15	100
type	1	7	16	3
product_services	1	4	1737	3244

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
revenue_omu	21515	0.22	1822155	18184433	3652.23	7676.36	16210.68	48327.66	310612303	▇▁▁▁▁

Show the code

DT::datatable(mc3_nodes)

Show the code

ggplot(data = mc3_nodes,
       aes(x = type)) +
  geom_bar()

Exploring Country of Origin

Show the code

nodes_country <- mc3_nodes %>%
  group_by(country, type) %>%
  summarise(count = n()) %>%
  ungroup()

Show the code

plot_company <- nodes_country %>%
  filter(type == "Company" &
           count > 150) %>%
  ggplot(aes(x = reorder(country, -count), y = count)) +
  geom_col() +
  ylim(0,4000) +
  geom_text(
    aes(label = count),
    vjust = -2
  ) +  
  labs(
    title = "Count of Company's Country of Origin", y= "Count", x = "Country", subtitle = "Companies predominantly from ZH, Oceanus, and Marebak"
  )

plot_owner <- nodes_country %>%
  filter(type == "Beneficial Owner") %>%
  ggplot(aes(x = reorder(country, -count), y = count)) +
  geom_col() +
  ylim(0,14000) +
  geom_text(
    aes(label = count),
    vjust = -2
  ) +  
  labs(
    title = "Count of Beneficial Owner's Country of Origin", y= "Count", x = "Country", subtitle = "Beneficial Ownders predominantly from ZH"
  )

plot_contacts <- nodes_country %>%
  filter(type == "Company Contacts") %>%
  ggplot(aes(x = reorder(country, -count), y = count)) +
  geom_col() +
  ylim(0,8000) +
  geom_text(
    aes(label = count),
    vjust = -2
  ) +  
  labs(
    title = "Count of Company Contacts' Country of Origin", y= "Count", x = "Country", subtitle = "Company Contact predominantly from ZH"
  ) 

plot_company / plot_owner / plot_contacts

Note

Despite most of the owners and and company contacts orginating from ZH, there count of country of origin of the company is more diverse, with ZH, Oceanus, and Marebak taking the top 3 spots. It is indicative of owners venturing out of their own countries to set up companies in other countries.

Exploring revenue of companies

Show the code

company_revenue <- mc3_nodes %>% 
  filter(type == "Company")

ggplot(company_revenue, 
       aes(y = revenue_omu)) +
 scale_y_continuous(
    limits = c(0, 200000),
    breaks = pretty_breaks(n = 5),
    labels = dollar_format())+
  geom_boxplot(width = 0.5,
               outlier.shape = NA, color = 'darkred') +
  stat_dots(color = 'blue') +
  coord_flip() + 
  labs(
    title = "Distribution of Revenue of Companies", y= "Revenue", x = "Count", subtitle = "Highly right skewed distribution of companies' revenue"
  )

Exploring the relationship between owners and companies:

Getting the number of owners each company has:

Show the code

edges_by_target <- mc3_edges %>%
  filter(type == 'Beneficial Owner') %>%
  group_by(source, type) %>%
  summarise(owner_count = n())%>%
  arrange(desc(owner_count))%>%
  ungroup()

Show the code

DT::datatable(edges_by_target)

The table above displays the number of owner each company has. It is postulated that companies with multiple beneficial owners has the oversight of many people, it is unlikely to be engaged in dubious activities, whereas companies which are sole proprietorships are at the bidding of that single beneficial owner.

Show the code

owner_count_df <- edges_by_target %>%
  group_by(owner_count) %>%
  summarize(count = n()) %>%
  arrange(desc(count)) %>%
  ungroup()

Show the code

filtered_data <- owner_count_df %>%
  filter(owner_count <= quantile(owner_count, 0.2))

ggplot(filtered_data, aes(x = factor(owner_count), y = count)) +
  geom_col() +
  ylim(0,7000) +
  geom_text(
    aes(label = count),
    vjust = -1,
    size = 3
  ) +  
  labs(
    title = "Count of Companies by number of owners", y= "Count of Companies", x = "Number of Owners", subtitle = "Majority of companies have only one owner"
  )

The above graph shows that 6415 companies are sole proprietorships.

Conclusion of EDA

Note

Initial Sensing:

Revenue is a good place to start to explore anomalous behaviour:
1. Beneficial owners who are sole owners of multiple companies
2. Companies with unreported revenue yet with many beneficial owners and company contacts - shows that the company is extensive, and should be reporting high revenue, yet the revenue is unaccounted for.
3. Companies which are owned by single individuals vs those owned by multiple owners - complete control vs having to get the consensus of multiple shareholders.

3. Network Visualisation and Analysis

3.1 Point of Interest 1:

The fish anomalous behavious that will be investigated would be sole beneficial owners of multiple companies, with companies having higher revenue being more suspicious. The reason being, there is no need for transparency and being accountable to shareholders for these companies. As such, they are less deterred from pursuing illegal activities given the lesser oversight.

Show the code

single_bowners <- mc3_edges %>%
  filter(type == "Beneficial Owner") %>%
  distinct(source, .keep_all = TRUE) 

single_bowner_count <- single_bowners %>%
  group_by(target) %>%
  mutate(count = n()) %>%
  filter(count >= 4) %>%
  ungroup()

Show the code

single_bowner_count_revenue <- left_join(single_bowner_count, mc3_nodes, by = c("source"="id")) %>%
  select(-type.y) %>%
  rename("type" = "type.x")

single_bowner_count_revenue1 <-  single_bowner_count_revenue %>%
  distinct() %>%
  rename("from" = "source",
         "to" = "target")

bowner_source <- single_bowner_count_revenue1 %>%
  distinct(from) %>%
  rename("id" = "from")

bowner_target <- single_bowner_count_revenue1 %>%
  distinct(to) %>%
  rename("id" = "to")

bowner_nodes_extracted <- rbind(bowner_source, bowner_target)

bowner_nodes_extracted$group <- ifelse(bowner_nodes_extracted$id %in% single_bowner_count_revenue$source, "Company", "Beneficial Owner")

Creating the visNetwork Graph

Show the code

visNetwork(
    bowner_nodes_extracted, 
    single_bowner_count_revenue1
  ) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Company",
            color = "lightblue") %>%
  visGroups(groupname = "Company Contact",
            color = "yellow") %>%
  visLegend() %>%
  visEdges(
    arrows = "to"
  ) %>%
  visOptions(
    highlightNearest = list(enabled = T, degree = 2, hover = T),
    nodesIdSelection = TRUE,
    selectedBy = "group",
    collapse = TRUE)

Note

With the possibility of lesser oversight, there is the chance that these companies may be participating in suspicious activity. However, it should be noted that the size as well and the fidelity of the nature of business of these companies is not available on the network graph. It should therefore be investigated in greater detail, before a solid conclusion can be formed. However, in the meantime, it is the exception and not the norm and should be monitored.

3.2 Point of Interest 2

The next suspicious behaviour that deserves investigating would be companies with many company contacts, that have missing revenue reported. There is a chance that these companies are in fact bring in substantial revenue but have undeclared their revenue.

Show the code

# Extract nodes that have unreported revenue
nodes_norev <- mc3_nodes %>%
  filter(is.na(revenue_omu))

nodes_norev_compcontact <- nodes_norev %>%
  filter(type == "Company Contacts") %>%
  distinct()

# Extracting edges that are company contacts
edges_norev <- mc3_edges %>%
  filter(type == "Company Contacts") %>%
  filter(source %in% nodes_norev_compcontact$id) %>%
  distinct() %>%
  rename("from" = "source",
         "to" = "target")

# Extract edges that have more than or equal to 3 company contacts
edges_norev_high <- edges_norev %>%
  group_by(from) %>%
  mutate(count = n()) %>%
  filter(count >= 3) %>%
  ungroup()

Show the code

# Get distinct Source and Target
norev_source <- edges_norev_high %>%
  distinct(from) %>%
  rename("id" = "from")

norev_target <- edges_norev_high %>%
  distinct(to) %>%
  rename("id" = "to")

Show the code

# Bind into single dataframe
nodes_norev1 <- bind_rows(norev_source, norev_target)

nodes_norev1$group <- ifelse(nodes_norev1$id %in% nodes_norev_compcontact$id, "Company Contact", "Company")

Creating the visNetwork graph

Show the code

visNetwork(
    nodes_norev1, 
    edges_norev_high
  ) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Company",
            color = "lightblue") %>%
  visGroups(groupname = "Company Contact",
            color = "yellow") %>%
  visLegend() %>%
  visEdges(
    arrows = "to"
  ) %>%
  visOptions(
    highlightNearest = list(enabled = T, degree = 2, hover = T),
    nodesIdSelection = TRUE,
    selectedBy = "group",
    collapse = TRUE)

Note

It is indeed suspicious that so many large companies have unreported revenue. The potulated size, given the lack of revenue data, can only be extrapolated using the number of contacts, and the number of beneficial owners. It should be noted that the number of contacts of these top few companies are similar to those of the top few companies with reported revenue. This should be investigated in further details with other forms of proxy information obtained and pieced together to determine what they can possibly be hiding.

3.3 Point of Interest 3

The last visual that i would be using to explore specifically fish-related anomalous behaviour would be the networks of biggest company-beneficial owner relationships of fish-related businesses. The reason that this is done is to compare and understand the typical network size of a fish-related business in terms of number of beneficial owners, and compare it with the industry standard.

Show the code

# Extract nodes that are fish-related
fish_nodes <- mc3_nodes %>%
  filter(grepl("fish", product_services, ignore.case = TRUE))

fish_nodes_bowners <- fish_nodes %>%
  filter(type == "Beneficial Owners") %>%
  distinct()

fish_nodes_companies <-fish_nodes %>%
  filter(type == "Company") %>%
  distinct()

# Extract edges that are fish related
edges_fish <- mc3_edges %>%
  filter(type %in% c("Company", "Beneficial Owner")) %>%
  filter(source %in% fish_nodes$id) %>%
  distinct() %>%
  rename("from" = "source",
         "to" = "target")

# Extract edges that have more than or equal to 8 links
edges_fish_high <- edges_fish %>%
  group_by(from) %>%
  mutate(count = n()) %>%
  filter(count >= 8) %>%
  ungroup()

Show the code

# Get distinct Source and Target
fish_source <- edges_fish_high %>%
  distinct(from) %>%
  rename("id" = "from")

fish_target <- edges_fish_high %>%
  distinct(to) %>%
  rename("id" = "to")

Show the code

# Bind into single dataframe
nodes_fish1 <- bind_rows(fish_source, fish_target)

nodes_fish1$group <- ifelse(nodes_fish1$id %in% fish_nodes_companies$id, "Company", "Beneficial Owner")

Creating the visNetwork Graph

Show the code

visNetwork(
    nodes_fish1, 
    edges_fish
  ) %>%
  visPhysics(solver = "forceAtlas2Based",
               forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Company",
            color = "yellow") %>%
  visGroups(groupname = "Beneficial Owner",
            color = "lightblue") %>%
  visLegend() %>%
  visEdges(
    arrows = "to"
  ) %>%
  visOptions(
    highlightNearest = list(enabled = T, degree = 2, hover = T),
    nodesIdSelection = TRUE,
    selectedBy = "group",
    collapse = TRUE)

Note

It is interesting to note that there are no personnel that are beneficial owners of more than 1 companies for these “large” or extensive companies. There is a possibility that they do not want to have multiple owners for fear of a conflict of interest or corporate espionage. While this is not necessarily anomalous behaviour, it is an interesting point to note, and it may even be representative of the different cartels in the fish industry.

1. The Task

1.1 Background

1.2 The Task in detail

2. Data Prep

2.1 Loading the requisite R libraries:

2.2 Importing the requisite JSON files

2.2 Extracting Edges

2.3 Extracting Nodes

3. Initial Data Exploration

3.1 Exploring Edges

3.2 Exploring the nodes data frame

3. Network Visualisation and Analysis

3.1 Point of Interest 1:

3.2 Point of Interest 2

3.3 Point of Interest 3

4. Conclusion